



from typing import List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case						=    logging.get_logger(__name__)

_snake_case						=    {
    "huggingface/autoformer-tourism-monthly": "https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json",
}



class     UpperCAmelCase_       (			a):
							lowerCamelCase__       =						'autoformer'
							lowerCamelCase__       =						{
							    'hidden_size': 'd_model',
							    'num_attention_heads': 'encoder_attention_heads',
							    'num_hidden_layers': 'encoder_layers',
							}







							def __init__(  self,     __a = None,     __a = None,     __a = "student_t",     __a = "nll",     __a = 1,     __a = [1, 2, 3, 4, 5, 6, 7],     __a = True,     __a = 0,     __a = 0,     __a = 0,     __a = 0,     __a = None,     __a = None,     __a = 64,     __a = 2,     __a = 2,     __a = 2,     __a = 2,     __a = 32,     __a = 32,     __a = "gelu",     __a = 0.1,     __a = 0.1,     __a = 0.1,     __a = 0.1,     __a = 0.1,     __a = 100,     __a = 0.02,     __a = True,     __a=True,     __a = 10,     __a = 25,     __a = 3,     **__a,     ):

														'''simple docstring'''

														_lowerCAmelCase	:				int					  =       prediction_length
														_lowerCAmelCase	:				Dict					  =       context_length if context_length is not None else prediction_length
														_lowerCAmelCase	:				Dict					  =       distribution_output
														_lowerCAmelCase	:				str					  =       loss
														_lowerCAmelCase	:				List[Any]					  =       input_size
														_lowerCAmelCase	:				Optional[int]					  =       num_time_features
														_lowerCAmelCase	:				Optional[Any]					  =       lags_sequence
														_lowerCAmelCase	:				Dict					  =       scaling
														_lowerCAmelCase	:				Tuple					  =       num_dynamic_real_features
														_lowerCAmelCase	:				Optional[Any]					  =       num_static_real_features
														_lowerCAmelCase	:				str					  =       num_static_categorical_features
														if cardinality is not None and num_static_categorical_features > 0:
																					if len(__a) != num_static_categorical_features:
																												raise ValueError(
																												    "The cardinality should be a list of the same length as `num_static_categorical_features`")
																					_lowerCAmelCase	:				Optional[Any]					  =       cardinality
														else:
																					_lowerCAmelCase	:				List[Any]					  =       [0]
														if embedding_dimension is not None and num_static_categorical_features > 0:
																					if len(__a) != num_static_categorical_features:
																												raise ValueError(
																												    "The embedding dimension should be a list of the same length as `num_static_categorical_features`")
																					_lowerCAmelCase	:				List[str]					  =       embedding_dimension
														else:
																					_lowerCAmelCase	:				Dict					  =       [min(50,     (cat + 1) // 2) for cat in self.cardinality]
														_lowerCAmelCase	:				Tuple					  =       num_parallel_samples

														# Transformer architecture configuration
														_lowerCAmelCase	:				Dict					  =       input_size * len(self.lags_sequence) + self._number_of_features
														_lowerCAmelCase	:				List[Any]					  =       d_model
														_lowerCAmelCase	:				int					  =       encoder_attention_heads
														_lowerCAmelCase	:				str					  =       decoder_attention_heads
														_lowerCAmelCase	:				Optional[int]					  =       encoder_ffn_dim
														_lowerCAmelCase	:				int					  =       decoder_ffn_dim
														_lowerCAmelCase	:				Any					  =       encoder_layers
														_lowerCAmelCase	:				Dict					  =       decoder_layers

														_lowerCAmelCase	:				str					  =       dropout
														_lowerCAmelCase	:				Dict					  =       attention_dropout
														_lowerCAmelCase	:				Tuple					  =       activation_dropout
														_lowerCAmelCase	:				List[str]					  =       encoder_layerdrop
														_lowerCAmelCase	:				List[Any]					  =       decoder_layerdrop

														_lowerCAmelCase	:				Optional[Any]					  =       activation_function
														_lowerCAmelCase	:				Optional[int]					  =       init_std

														_lowerCAmelCase	:				int					  =       use_cache

														# Autoformer
														_lowerCAmelCase	:				int					  =       label_length
														_lowerCAmelCase	:				Union[str, Any]					  =       moving_average
														_lowerCAmelCase	:				List[str]					  =       autocorrelation_factor

														super().__init__(is_encoder_decoder=__a,     **__a)







							@property
							def   snake_case__ (  self):

														'''simple docstring'''

														return (
														    sum(self.embedding_dimension)
														    + self.num_dynamic_real_features
														    + self.num_time_features
														    + self.num_static_real_features
														    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
														)






'''simple docstring'''


from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
UpperCAmelCase_					=   Lock()


def     SCREAMING_SNAKE_CASE							(   a_						:				str					,       a_						:				Tuple					,       a_						:				Dict					,       a_						:				Optional[Any]					,       a_						:				str					,       a_						:				Any					,       a_						:				Optional[Any]      ):
  global process_lock

  # we perform n swaps since after n swaps we know we are sorted
  # we *could* stop early if we are sorted already, but it takes as long to
  # find out we are sorted as it does to sort the list with this algorithm
  for i in range(0					,       10      ):
    if (i + position) % 2 == 0 and r_send is not None:
      # send your value to your right neighbor
      process_lock.acquire()
      r_send[1].send(a_      )
      process_lock.release()

      # receive your right neighbor's value
      process_lock.acquire()
      __a								=       rr_cv[0].recv()
      process_lock.release()

      # take the lower value since you are on the left
      __a								=       min(a_					,       a_      )
    elif (i + position) % 2 != 0 and l_send is not None:
      # send your value to your left neighbor
      process_lock.acquire()
      l_send[1].send(a_      )
      process_lock.release()

      # receive your left neighbor's value
      process_lock.acquire()
      __a								=       lr_cv[0].recv()
      process_lock.release()

      # take the higher value since you are on the right
      __a								=       max(a_					,       a_      )
    # after all swaps are performed, send the values back to main
  result_pipe[1].send(a_      )


def     SCREAMING_SNAKE_CASE							(   a_						:				List[str]      ):
  __a								=       []
  __a								=       []
  # initialize the list of pipes where the values will be retrieved
  for _ in arr:
    result_pipe.append(Pipe()      )
  # creates the processes
  # the first and last process only have one neighbor so they are made outside
  # of the loop
  __a								=       Pipe()
  __a								=       Pipe()
  process_array_.append(
      Process(
          target=a_					,       args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])					,       )      )
  __a								=       temp_rs
  __a								=       temp_rr

  for i in range(1					,       len(a_      ) - 1      ):
    __a								=       Pipe()
    __a								=       Pipe()
    process_array_.append(
        Process(
            target=a_					,       args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])					,       )      )
    __a								=       temp_rs
    __a								=       temp_rr

  process_array_.append(
      Process(
          target=a_					,       args=(
              len(a_      ) - 1,
              arr[len(a_      ) - 1],
              temp_ls,
              None,
              temp_lr,
              None,
              result_pipe[len(a_      ) - 1],
          )					,       )      )

  # start the processes
  for p in process_array_:
    p.start()

  # wait for the processes to end and write their values to the list
  for p in range(0					,       len(a_      )      ):
    __a								=       result_pipe[p][0].recv()
    process_array_[p].join()
  return arr


def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       list(range(10					,       0					,       -1      )      )
  print('Initial List'      )
  print(*a_      )
  __a								=       odd_even_transposition(a_      )
  print('Sorted List\n'      )
  print(*a_      )


if __name__ == "__main__":
  main()



